Computers and computing devices have a long history dating back thousands of years to places including ancient Babylonia, Greece, Rome, China, and Arabia. During the industrial revolution, Charles Babbage and Ada Lovelace developed concepts relating to mechanical computing machines and programming. However, the concept of the general purpose computer did not emerge until 1936 when Alan Turing published his seminal paper on computing theory titled “On Computable Numbers, With An Application To The Entscheidungsproblem.” With this event, humanity crossed the digital divide bringing us to the point today where the volume of digital data generated in a single year surpasses the volume of data created in all previous human history. From the very moment the first military and business applications emerged, people have been challenged to analyze, interpret, and make decisions based on digital data. The general categories of these efforts have included Decision Support Systems, Knowledge Management Systems, Content Management Systems, Data Warehousing and Data Mining, Artificial Intelligence Systems, and Search Engines, to name a few.
More particularly, a Decision Support System (DSS) typically incorporates techniques that leverage existing processes and targets with an attempt to optimize specific point decisions in a management model. DSS-based solutions tend to be highly dependent upon the underlying application and can provide insight and guidance only within tightly bound constraints. In a Knowledge Management (KM) System, efforts generally aim to capture the human learning and understanding of an organization through cultural assessment of the information required in the performance of specific jobs and roles. These solutions are more focused on community, sharing, and preserving the human capital of an organization and less so on analyzing and interpreting the information to gain new knowledge or insights. Content Management (CM) Systems are usually used to capture, archive and manage the various forms of structured and unstructured data important to an organization and make that information available to individuals and other systems. Although CM-based solutions can provide an exhaustive repository of information, CM-based solutions typically make no effort to understand or illuminate the underling meaning of the data.
Other proposed solutions, such as Data Warehousing (DW) and Data Mining (DM) (also known as Business Intelligence), fall into the class of applications that represents the initial efforts to use analytic tools and processes to uncover and create new knowledge from existing data. The challenges include difficulties to build logical models that reflect operational reality and the inability to manage data quality in transition from source systems to centralized repositories. Artificial Intelligence (AI) Systems introduce aspects related to machine learning and the desire to enable computing processes that have the ability to self-organize and independently recognize patterns and structures that may be relevant for human consideration. Search Engines include some of the most important and successful technology solutions that the world has seen. For example, search engines can provide immediate, real-time access to the substantial information maintained on the World Wide Web and other data sources, typically using proprietary algorithms to assess relevance of that information based on aggregated user activity. While profoundly significant, this class of solutions does not directly or transparently provide deeper meaning, logic, or insights from the information, nor does this class of solutions allow for the introduction of personalized structures or access to private data.
Accordingly, existing efforts to analyze, interpret, and make decisions based on digital data, including the solutions discussed above, have insufficiently dealt with the way that people really want to access the data in the world or enable the true opportunities for discovery, verification, association, and/or prediction that this worldwide body of data can provide. Although other concepts such as Web 3.0, Semantic Web, and Knowledge Engines are emerging, at the present point in time there are no systems or methods that have been successful in delivering on the underlying promises of an “Intelligent Web.” The evolution of the Internet has moved from Web 1.0 being static information, to Web 2.0 incorporating greater interactivity and social connectivity, and now to the vision of Web 3.0 being based on natural language processing, autonomy and intelligence. The Semantic Web is a similar or equivalent vision of an Internet based on frameworks and standards where there is an autonomous connectivity of machines and applications across technology, informational and community boundaries. The Knowledge Engine is an effort to bring trust and credibility to web delivered information. While progress is being made and clearly the ways and uses of information is continuing to evolve, as of yet these concepts have not matured to the point of technical, economic or social viability.
One key reason is the lack of an underlying informational framework that provides the foundational logic and structure to enable contextual definition and interpretation of the information contained on the Internet. For example, the technical architecture of the Internet has generally been based on two key frameworks. The physical Internet Protocol (IP) addresses are allocated and maintained by the Internet Assigned Numbers Authority (IANA) under the governance of the Internet Corporation for Assigned Names and Numbers (ICANN). The Domain Name System (DNS) and Uniform Resource Locators (URLs) are maintained by multiple domain name registrars also under the governance of ICANN. These frameworks respectively enable the identification and connections of devices (colloquially the Internet of Things (IoT)) and web pages (the World Wide Web (WWW)). Neither of these frameworks have the capability to manage the contextual meaning or content of information.
Thus there exists a need for a system and a set of methods that can provide the context of logical semantic meaning and deliver the tools and capabilities to create and manage knowledge, insights, and intelligence from large global data collections. Information is data in context. Wisdom is information with understanding. The present disclosure provides the foundational capabilities for contextual navigation that enables these transformations of awareness, insight, and knowledge. In particular, as will be described in further detail herein, the present disclosure relates to an information context engine that may implement various logical semantic data structures and processing algorithms, which may be integrated with bi-directional HyperText Markup Language (HTML) and Uniform Resource Locator (URL) links to and from World Wide Web structures and thereby deliver the ability to discover, explore and analyze information in novel, innovative, and useful ways.